AI is rapidly reshaping biomedical research, but practitioners agree that AI’s success depends on more than advanced algorithms.

This research assesses data provenance in widely used health datasets, revealing flaws that could undermine clinical prediction models and patient care.

Genomic surveillance—the process of monitoring and sequencing pathogens—is one of the most important tools for detecting ...

A large study applies advanced machine learning to identify shared risk factors and predictors of disease onset in patients with epilepsy and depression.

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

Computational point-of-care sensors can significantly improve access to diagnostics by enabling rapid patient testing outside centralized medical facilities. These tests rely on machine learning ...

A machine learning (ML) tool that analyses electronic health record data, test results, and patient demographics can help clinicians identify people at high risk of hepatocellular carcinoma (HCC), the ...

A machine learning model that analyzes patient demographics, electronic health record data, and routine blood test results predicted a patient's risk of hepatocellular carcinoma (HCC), the most common ...

A team of Stanford Medicine researchers have introduced SleepFM Clinical, a multimodal sleep foundation model that learns from clinical polysomnography and predicts long term disease risk from a ...

A new machine learning model developed by The George Institute for Global Health can successfully predict heart disease risk in women by analyzing mammograms. The findings were published today in ...

Athletes who suffer a concussion have a serious risk of reinjury after returning to play, but identifying which athletes are most vulnerable has always been a bit of a mystery, until now. “This is due ...